Comparison of AR parametric methods with subspace-based methods for EMG signal classification using stand-alone and merged neural network models

Authors : MEHMET RECEP BOZKURT, ABDÜLHAMİT SUBAŞI, ETEM KÖKLÜKAYA, MUSTAFA YILMAZ

Pages : 1547-1559

View : 16 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This research introduces an electromyogram insert ignore into journalissuearticles values(EMG); pattern classification of individual motor unit action potentials insert ignore into journalissuearticles values(MUPs); from intramuscular electromyographic signals. The presented technique automatically classifies EMG patterns into healthy, myopathic, or neurogenic categories. To extract a feature vector from the EMG signal, we use different autoregressive insert ignore into journalissuearticles values(AR); parametric methods and subspace-based methods. The proposal was validated using EMG recordings composed of 1200 EMG patterns obtained from 7 healthy, 7 myopathic, and 13 neurogenic-disordered people. A feedforward error backpropagation artificial neural network insert ignore into journalissuearticles values(FEBANN); and combined neural network insert ignore into journalissuearticles values(CNN); were used for classification, where the success rate was slightly higher in CNN. Among the different AR and subspace methods used in this study, the highest performance was obtained with the eigenvector method. The following rates were the results achieved by using the CNN. The correct classification rate for EMG patterns was 97% for healthy, 93% for myopathic, and 92% for neurogenic patterns. The obtained accuracy for EMG signal classification is approximately 94% for CNN. The rates for FEBANN were as follows: 97% for healthy patterns, 92% for myopathic patterns, and 91% for neurogenic patterns. The obtained accuracy was 93.3%. By directly using raw EMG signals, EMG classifications of healthy, myopathic, or neurogenic classes are automatically addressed.
Keywords : Electromyography, motor unit potentials, autoregressive spectral estimation method, subspace based methods, combined neural network